Data-dependent Metric Filtering

J 2022

Data-dependent Metric Filtering

MÍČ, Vladimír and Pavel ZEZULA

Basic information

Original name

Data-dependent Metric Filtering

Authors

MÍČ, Vladimír (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, guarantor, belonging to the institution)

Edition

Information systems, 2022, 0306-4379

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 3.700

RIV identification code

RIV/00216224:14330/22:00125539

Organization unit

Faculty of Informatics

DOI

http://dx.doi.org/10.1016/j.is.2021.101980

UT WoS

001133975200010

Keywords in English

Metric Space Searching;Similarity Search;Metric Filtering;Data Dependent Filtering

Abstract

V originále

Filtering is a fundamental strategy of metric similarity indexes to minimise the number of computed distances. Given a triplet of objects for which distances of two pairs are known, the lower and upper bounds on the third distance can be determined using the triangle inequality property. Obviously, tightness of the bounds is crucial for efficiency reasons — the more precise the estimation, the more distance computations can be avoided, and the more efficient the search is. We show that it is not necessary to consider arbitrary angles in triangles formed by pairwise distances of three objects, as specific range of possible angles is data dependent. When considering realistic ranges of angles, the bounds on distances can be much more tight and filtering much more effective. We formalise the problem of the data dependent estimation of bounds on distances and deeply analyse limited angles in triangles of distances. We justify the potential of the data dependent metric filtering both, analytically and experimentally, executing many distance estimations on several real-life datasets.

Links

EF16_019/0000822, research and development project

Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

Citovat

MÍČ, Vladimír and Pavel ZEZULA. Data-dependent Metric Filtering. Information systems. 2022, vol. 108, 12.5.2022, p. "101980", 21 pp. ISSN 0306-4379. Available from: https://dx.doi.org/10.1016/j.is.2021.101980.

@article{1841617,
   author = {Míč, Vladimír and Zezula, Pavel},
   article_number = {12.5.2022},
   doi = {http://dx.doi.org/10.1016/j.is.2021.101980},
   keywords = {Metric Space Searching;Similarity Search;Metric Filtering;Data Dependent Filtering},
   language = {eng},
   issn = {0306-4379},
   journal = {Information systems},
   title = {Data-dependent Metric Filtering},
   url = {https://www.sciencedirect.com/science/article/pii/S0306437921001666},
   volume = {108},
   year = {2022}
}

TY  - JOUR
ID  - 1841617
AU  - Míč, Vladimír - Zezula, Pavel
PY  - 2022
TI  - Data-dependent Metric Filtering
JF  - Information systems
VL  - 108
IS  - 12.5.2022
SP  - "101980"
EP  - "101980"
SN  - 03064379
KW  - Metric Space Searching;Similarity Search;Metric Filtering;Data Dependent Filtering
UR  - https://www.sciencedirect.com/science/article/pii/S0306437921001666
N2  - Filtering is a fundamental strategy of metric similarity indexes to minimise the number of computed distances. Given a triplet of objects for which distances of two pairs are known, the lower and upper bounds on the third distance can be determined using the triangle inequality property. Obviously, tightness of the bounds is crucial for efficiency reasons — the more precise the estimation, the more distance computations can be avoided, and the more efficient the search is. We show that it is not necessary to consider arbitrary angles in triangles formed by pairwise distances of three objects, as specific range of possible angles is data dependent. When considering realistic ranges of angles, the bounds on distances can be much more tight and filtering much more effective. We formalise the problem of the data dependent estimation of bounds on distances and deeply analyse limited angles in triangles of distances. We justify the potential of the data dependent metric filtering both, analytically and experimentally, executing many distance estimations on several real-life datasets.
ER  -

MÍČ, Vladimír and Pavel ZEZULA. Data-dependent Metric Filtering. \textit{Information systems}. 2022, vol.~108, 12.5.2022, p.~''101980'', 21 pp. ISSN~0306-4379. Available from: https://dx.doi.org/10.1016/j.is.2021.101980.

Detailed Information on Publication Record